Advanced Applications in Bayesian Networks

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 27 June 2024 | Viewed by 187

Special Issue Editors

Department of Computer Science, The University of Manchester, Oxford Rd., Manchester M13 9PL, UK
Interests: causal discovery; causal inference; causal machine learning; bayesian networks

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Guest Editor
Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Interests: bayesian networks; stochastic neural networks; transfer learning
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Special Issue Information

Dear Colleagues,

We kindly invite you to submit to our latest Special Issue on "Advanced Applications in Bayesian Networks", which aims to explore the cutting-edge methodologies and innovative applications of Bayesian networks in various fields, such as environmental modelling, bioinformatics, decision support systems, traffic and transportation, educational assessment, risk analysis, robot control, fault diagnosis, recommendation systems, forensics, etc. Bayesian networks (BNs) are a type of probabilistic graphical model that represent a set of variables and their conditional dependencies via a directed acyclic graph. BNs efficiently manage uncertainty and complexity, enable causal reasoning, and update beliefs dynamically with new evidence in decision-making processes. Despite their widespread use, there are continuous advancements in their application, addressing complex problems such as uncertainty quantification, causal inference, and predictive modeling.

This Special Issue will bridge the gap between foundational Bayesian network theory and practical applications, offering a platform for novel research that pushes the boundaries of current capabilities. It will examine how recent improvements in learning algorithms, computational power, and data-driven approaches can enhance the performance and applicability of Bayesian networks.

By showcasing state-of-the-art applications, this issue will complement the existing literature by focusing on real-world challenges. It will offer readers insights into progressive methodologies, underscore unexplored areas, bridge the gap between theoretical underpinnings and practical applications, and foster interdisciplinary collaboration.

Dr. Zhigao Guo
Dr. Yun Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • probabilistic graphical models
  • causal discovery
  • causal inference
  • application
  • causality

Published Papers

This special issue is now open for submission.
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